抗击超级细菌:耐药酶的起源研究和变异预测

随着抗菌药物在临床和农、林、牧、副、鱼业上广泛应用,细菌的耐药性现象越来越严重,新出现的携带新德里金属β-内酰胺酶-1(new delhi metallo-β-lactamase-1,NDM-1)基因的超级细菌,几乎对临床上所有抗菌药物均具有耐药性,而且这一耐药基因位于质粒上,会在细菌中水平转移,对人类健康具有潜在的极大威胁。更严重的是,目前尚无法预知今后将出现何种新的耐药基因,但抗菌药物选择性压力的结果必然导致新的更为棘手的耐药性出现。国内外已有一些探索性工作试图揭示耐药基因的起源,并着力于预测这些基因今后可能的演变方向,以期在新的耐药性出现前作出应对。该文将以β-内酰胺耐药酶为主,概述国内外在耐药酶起源、自然进化、模拟进化等领域的研究进展,希望能为抗击超级细菌提供新的实验思路。     

Sample size for two‐stage studies with maintenance therapy

An adaptive treatment strategy (ATS) is defined as a sequence of treatments and intermediate responses. ATS' arise when chronic diseases such as cancer and depression are treated over time with various treatment alternatives depending on intermediate responses to earlier treatments. Clinical trials are often designed to compare ATSs based on appropriate designs such as sequential randomization designs. Although recent literature provides statistical methods for analyzing data from such trials, very few articles have focused on statistical power and sample size issues. This paper presents a sample size formula for comparing the survival probabilities under two treatment strategies sharing same initial, but different maintenance treatment. The formula is based on the large sample properties of inverse‐probability‐weighted estimator. Simulation study shows strong evidence that the proposed sample size formula guarantees desired power, regardless of the true distributions of survival times. Copyright © 2009 John Wiley & Sons, Ltd.     

Unified Sample Size Calculations Using the Competing Probability

Sample size formulas are functions of two categories of quantities. The first category does not depend on the analysis variables and contains randomization ratio, Type I error rate, and power. The second category depends on the analysis variables and includes means and standard deviations for the two-sample t-test, competing probabilities for the Wilcoxon–Mann–Whitney test, and median survival times for the logrank test. Quantities in the second category depend on subject matter knowledge and are much harder to specify than those in the first category. In addition, these quantities have different interpretations. In this article we propose using the competing probability as the only second category quantity when calculating sample sizes for the aforementioned three commonly used tests in clinical trials. Doing so unifies the interpretation of and sheds new light on the two-sample t-tests and the logrank tests in terms of sample size calculations.     

Uncovering Hereditary Tumor Syndromes: Emerging Role of Surgical Pathology

With the increased use of modern next generation sequencing technologies in routine molecular pathology practice, the proportion of cancer cases with a definite or probable hereditary background seems to be steadily increasing. Currently, it is assumed that ≥10% of all malignancies develop in the setting of germline predisposition. Diagnosis and recognition of cancer predisposition syndromes relies not rarely on distinctive histopathological features that proved to be highly valuable and reproducible in uncovering those diseases that would otherwise have gone undetected by clinicians as being hereditary in nature. This is especially true in case of new mutations without suspicious family history. Example of such entities are fumarate hydratase-deficient renal cell carcinoma (RCC), succinate dehydrogenase-deficient RCC, hereditary gastrointestinal stromal tumor syndromes and many other diseases. It is remarkable that many of these inherited cancer syndromes do present as unifocal disease with highly variable age of onset so that many of them are misinterpreted as sporadic on clinical grounds. Availability of specialized cancer screening programs and disease-specific follow-up schemes for several hereditary cancer syndromes encourages the recognition of such disorders, so that "at risk patients" can be enrolled in such programs for early detection and timely intervention/ treatment of these malignancies which are in the majority of cases aggressive. In several conditions, as in familial adenomatous polyposis coli (FAP), well established prophylactic surgical interventions may be adopted to prevent the disease manifestations, highlighting the importance of the timely recognition of these potentially life-limiting neoplasms. In this review, the clinicopathological, demographic and histological features that are considered highly suggestive of a hereditary basis of "a neoplasm under consideration" are highlighted and discussed briefly. The details of some of these entities are in addition dealt with in reviews devoted to them in this special issue     

Gene variants influencing measures of inflammation or predisposing to autoimmune and inflammatory diseases are not associated with the risk of type 2 diabetes

Aims/hypothesis  There are strong associations between measures of inflammation and type 2 diabetes, but the causal directions of these associations are not known. We tested the hypothesis that common gene variants known to alter circulating levels of inflammatory proteins, or known to alter autoimmune-related disease risk, influence type 2 diabetes risk. Methods  We selected 46 variants: (1) eight variants known to alter circulating levels of inflammatory proteins, including those in the IL18, IL1RN, IL6R, MIF, PAI1 (also known as SERPINE1) and CRP genes; and (2) 38 variants known to predispose to autoimmune diseases, including type 1 diabetes. We tested the associations of these variants with type 2 diabetes using a meta-analysis of 4,107 cases and 5,187 controls from the Wellcome Trust Case Control Consortium, the Diabetes Genetics Initiative, and the Finland–United States Investigation of NIDDM studies. We followed up associated variants (p < 0.01) in a further set of 3,125 cases and 3,596 controls from the UK. Results  We found no evidence that inflammatory or autoimmune disease variants are associated with type 2 diabetes (at p ≤ 0.01). The OR observed between the variant altering IL-18 levels, rs2250417, and type 2 diabetes (OR 1.00 [95% CI 0.99–1.03]), is much lower than that expected given (1) the effect of the variant on IL-18 levels (0.28 SDs per allele); and (2) estimates, based on other studies, of the correlation between IL-18 levels and type 2 diabetes risk (approximate OR 1.15 [95% CI 1.09–1.21] per 0.28 SD increase in IL-18 levels). Conclusions/interpretation  Our study provided no evidence that variants known to alter measures of inflammation, autoimmune or inflammatory disease risk, including type 1 diabetes, alter type 2 diabetes risk. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorised users.     

High-sensitivity C-reactive protein,low-grade systemic inflammation and type 2 diabetes mellitus: A two-sample Mendelian randomization study

Background and aims

The role of inflammation in type 2 diabetes mellitus (T2D) remains unclear. We investigated the associations of high sensitivity C-reactive protein (hsCRP) concentration with T2D and glycemic traits using two-sample Mendelian Randomization.

Contribution of metabolic risk factors and lifestyle behaviors to cardiovascular disease: A mendelian randomization study

Background and aimsEtiologic associations between some modifiable factors (metabolic risk factors and lifestyle behaviors) and cardiovascular disease (CVD) remain unclear. To identify targets for CVD prevention, we evaluated the causal associations of these factors with coronary artery disease (CAD) and ischemic stroke using a two-sample Mendelian randomization (MR) method.Methods and resultsPreviously published genome-wide association studies (GWASs) for blood pressure (BP), glucose, lipids, overweight, smoking, alcohol intake, sedentariness, and education were used to identify instruments for 15 modifiable factors. We extracted effects of the genetic variants used as instruments for the exposures on coronary artery disease (CAD) and ischemic stroke from large GWASs (N = 60 801 cases/123 504 controls for CAD and N = 40 585 cases/406 111 controls for ischemic stroke). Genetically predicted hypertension (CAD: OR, 5.19 [95% CI, 4.21–6.41]; ischemic stroke: OR, 4.92 [4.12–5.86]), systolic BP (CAD: OR, 1.03 [1.03–1.04]; ischemic stroke: OR, 1.03 [1.03–1.03]), diastolic BP (CAD: OR, 1.05 [1.05–1.06]; ischemic stroke: OR, 1.05 [1.04–1.05]), type 2 diabetes (CAD: OR, 1.11 [1.08–1.15]; ischemic stroke: OR, 1.07 [1.04-1.10]), smoking initiation (CAD: OR, 1.26 [1.18–1.35]; ischemic stroke: OR, 1.24 [1.16–1.33]), educational attainment (CAD: OR, 0.62 [0.58–0.66]; ischemic stroke: OR, 0.68 [0.63–0.72]), low-density lipoprotein cholesterol (CAD: OR, 1.55 [1.41–1.71]), high-density lipoprotein cholesterol (CAD: OR, 0.82 [0.74–0.91]), triglycerides (CAD: OR, 1.29 [1.14–1.45]), body mass index (CAD: OR, 1.25 [1.19–1.32]), and alcohol dependence (OR, 1.04 [1.03–1.06]) were causally related to CVD.ConclusionThis systematic MR study identified 11 modifiable factors as causal risk factors for CVD, indicating that these factors are important targets for preventing CVD.     

Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants

We compared whole-exome sequencing (WES) and whole-genome sequencing (WGS) in six unrelated individuals. In the regions targeted by WES capture (81.5% of the consensus coding genome), the mean numbers of single-nucleotide variants (SNVs) and small insertions/deletions (indels) detected per sample were 84,192 and 13,325, respectively, for WES, and 84,968 and 12,702, respectively, for WGS. For both SNVs and indels, the distributions of coverage depth, genotype quality, and minor read ratio were more uniform for WGS than for WES. After filtering, a mean of 74,398 (95.3%) high-quality (HQ) SNVs and 9,033 (70.6%) HQ indels were called by both platforms. A mean of 105 coding HQ SNVs and 32 indels was identified exclusively by WES whereas 692 HQ SNVs and 105 indels were identified exclusively by WGS. We Sanger-sequenced a random selection of these exclusive variants. For SNVs, the proportion of false-positive variants was higher for WES (78%) than for WGS (17%). The estimated mean number of real coding SNVs (656 variants, ∼3% of all coding HQ SNVs) identified by WGS and missed by WES was greater than the number of SNVs identified by WES and missed by WGS (26 variants). For indels, the proportions of false-positive variants were similar for WES (44%) and WGS (46%). Finally, WES was not reliable for the detection of copy-number variations, almost all of which extended beyond the targeted regions. Although currently more expensive, WGS is more powerful than WES for detecting potential disease-causing mutations within WES regions, particularly those due to SNVs.Whole-exome sequencing (WES) is routinely used and is gradually being optimized for the detection of rare and common genetic variants in humans (1–8). However, whole-genome sequencing (WGS) is becoming increasingly attractive as an alternative, due to its broader coverage and decreasing cost (9–11). It remains difficult to interpret variants lying outside the protein-coding regions of the genome. Diagnostic and research laboratories, whether public or private, therefore tend to search for coding variants, most of which can be detected by WES, first. Such variants can also be detected by WGS, and several studies previously compared WES and WGS for different types of variations and/or in different contexts (9, 11–16), but none of them in a really comprehensive manner. Here, we compared WES and WGS, in terms of detection rates and quality, for single-nucleotide variants (SNVs), small insertions/deletions (indels), and copy-number variants (CNVs) within the regions of the human genome covered by WES, using the most recent next-generation sequencing (NGS) technologies. We aimed to identify the most efficient and reliable approach for identifying these variants in coding regions of the genome, to define the optimal analytical filters for decreasing the frequency of false-positive variants, and to characterize the genes that were either hard to sequence by either approach or were poorly covered by WES kits.